{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1035 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mplfinance as mpf\n",
    "import pandas as pd \n",
    "\n",
    "myData = pd.read_csv(\"issue77_eurusd.csv\")\n",
    "myData['Date and Time'] = pd.to_datetime(myData['Gmt time'],format='%d.%m.%Y %H:%M:%S.000')\n",
    "myData.set_index('Date and Time', inplace=True)\n",
    "myData = myData.iloc[-24:]\n",
    "\n",
    "\n",
    "line = mpf.make_addplot(myData[\"High\"],scatter=False) #No issues drawing a line\n",
    "scatter = mpf.make_addplot(myData[\"High\"],scatter=True,secondary_y=False) #Issues drawing scatter\n",
    "\n",
    "mpf.plot(myData,\n",
    "         type = 'candle',\n",
    "         style='yahoo', \n",
    "         addplot= line,\n",
    "         figscale=1.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0.3\n",
      "0.12.3a5\n"
     ]
    }
   ],
   "source": [
    "print(pd.__version__)\n",
    "print(mpf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.127852</td>\n",
       "      <td>1.128042</td>\n",
       "      <td>1.127682</td>\n",
       "      <td>1.127869</td>\n",
       "      <td>0.002365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.127350</td>\n",
       "      <td>1.127600</td>\n",
       "      <td>1.127250</td>\n",
       "      <td>1.127350</td>\n",
       "      <td>0.001135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.127690</td>\n",
       "      <td>1.127890</td>\n",
       "      <td>1.127592</td>\n",
       "      <td>1.127722</td>\n",
       "      <td>0.001897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.127855</td>\n",
       "      <td>1.128025</td>\n",
       "      <td>1.127695</td>\n",
       "      <td>1.127875</td>\n",
       "      <td>0.002292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.127992</td>\n",
       "      <td>1.128238</td>\n",
       "      <td>1.127825</td>\n",
       "      <td>1.127992</td>\n",
       "      <td>0.002807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.128360</td>\n",
       "      <td>1.128420</td>\n",
       "      <td>1.128060</td>\n",
       "      <td>1.128350</td>\n",
       "      <td>0.003500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Open       High        Low      Close     Volume\n",
       "count  24.000000  24.000000  24.000000  24.000000  24.000000\n",
       "mean    1.127852   1.128042   1.127682   1.127869   0.002365\n",
       "std     0.000238   0.000229   0.000213   0.000219   0.000646\n",
       "min     1.127350   1.127600   1.127250   1.127350   0.001135\n",
       "25%     1.127690   1.127890   1.127592   1.127722   0.001897\n",
       "50%     1.127855   1.128025   1.127695   1.127875   0.002292\n",
       "75%     1.127992   1.128238   1.127825   1.127992   0.002807\n",
       "max     1.128360   1.128420   1.128060   1.128350   0.003500"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date and Time\n",
       "2019-04-09 12:00:00    1.12769\n",
       "2019-04-09 12:05:00    1.12789\n",
       "2019-04-09 12:10:00    1.12776\n",
       "2019-04-09 12:15:00    1.12789\n",
       "2019-04-09 12:20:00    1.12790\n",
       "2019-04-09 12:25:00    1.12777\n",
       "2019-04-09 12:30:00    1.12800\n",
       "2019-04-09 12:35:00    1.12823\n",
       "2019-04-09 12:40:00    1.12796\n",
       "2019-04-09 12:45:00    1.12786\n",
       "2019-04-09 12:50:00    1.12760\n",
       "2019-04-09 12:55:00    1.12790\n",
       "2019-04-09 13:00:00    1.12837\n",
       "2019-04-09 13:05:00    1.12836\n",
       "2019-04-09 13:10:00    1.12805\n",
       "2019-04-09 13:15:00    1.12809\n",
       "2019-04-09 13:20:00    1.12829\n",
       "2019-04-09 13:25:00    1.12829\n",
       "2019-04-09 13:30:00    1.12826\n",
       "2019-04-09 13:35:00    1.12813\n",
       "2019-04-09 13:40:00    1.12842\n",
       "2019-04-09 13:45:00    1.12821\n",
       "2019-04-09 13:50:00    1.12812\n",
       "2019-04-09 13:55:00    1.12797\n",
       "Name: High, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scatter['data'].iloc[ 0] = np.nan\n",
    "#scatter['data'].iloc[10] = np.nan\n",
    "#scatter['data'].iloc[-1] = np.nan\n",
    "scatter['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x414 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x414 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(myData,\n",
    "         type = 'candle',\n",
    "         style='yahoo', \n",
    "         addplot= scatter,\n",
    "        )\n",
    "mpf.plot(myData,\n",
    "         type = 'candle',\n",
    "         style='yahoo', \n",
    "         addplot= scatter,\n",
    "         set_ylim=(1.1271,1.1286)\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x414 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x414 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(myData,\n",
    "         type = 'candle',\n",
    "         style='yahoo', \n",
    "         addplot= scatter,\n",
    "         volume=True\n",
    "        )\n",
    "mpf.plot(myData,\n",
    "         type = 'candle',\n",
    "         style='yahoo', \n",
    "         addplot= scatter,\n",
    "         volume=True,\n",
    "         set_ylim=(1.1271,1.1286),\n",
    "         set_ylim_panelB=(-0.005,0.006),\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': Date and Time\n",
       " 2019-04-09 12:00:00    1.12769\n",
       " 2019-04-09 12:05:00    1.12789\n",
       " 2019-04-09 12:10:00    1.12776\n",
       " 2019-04-09 12:15:00    1.12789\n",
       " 2019-04-09 12:20:00    1.12790\n",
       " 2019-04-09 12:25:00    1.12777\n",
       " 2019-04-09 12:30:00    1.12800\n",
       " 2019-04-09 12:35:00    1.12823\n",
       " 2019-04-09 12:40:00    1.12796\n",
       " 2019-04-09 12:45:00    1.12786\n",
       " 2019-04-09 12:50:00    1.12760\n",
       " 2019-04-09 12:55:00    1.12790\n",
       " 2019-04-09 13:00:00    1.12837\n",
       " 2019-04-09 13:05:00    1.12836\n",
       " 2019-04-09 13:10:00    1.12805\n",
       " 2019-04-09 13:15:00    1.12809\n",
       " 2019-04-09 13:20:00    1.12829\n",
       " 2019-04-09 13:25:00    1.12829\n",
       " 2019-04-09 13:30:00    1.12826\n",
       " 2019-04-09 13:35:00    1.12813\n",
       " 2019-04-09 13:40:00    1.12842\n",
       " 2019-04-09 13:45:00    1.12821\n",
       " 2019-04-09 13:50:00    1.12812\n",
       " 2019-04-09 13:55:00    1.12797\n",
       " Name: High, dtype: float64,\n",
       " 'scatter': True,\n",
       " 'panel': 'main',\n",
       " 'marker': 'o',\n",
       " 'markersize': 18,\n",
       " 'color': None,\n",
       " 'linestyle': None,\n",
       " 'secondary_y': False}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2019-04-09T12:00:00.000000000', '2019-04-09T12:05:00.000000000',\n",
       "       '2019-04-09T12:10:00.000000000', '2019-04-09T12:15:00.000000000',\n",
       "       '2019-04-09T12:20:00.000000000', '2019-04-09T12:25:00.000000000',\n",
       "       '2019-04-09T12:30:00.000000000', '2019-04-09T12:35:00.000000000',\n",
       "       '2019-04-09T12:40:00.000000000', '2019-04-09T12:45:00.000000000',\n",
       "       '2019-04-09T12:50:00.000000000', '2019-04-09T12:55:00.000000000',\n",
       "       '2019-04-09T13:00:00.000000000', '2019-04-09T13:05:00.000000000',\n",
       "       '2019-04-09T13:10:00.000000000', '2019-04-09T13:15:00.000000000',\n",
       "       '2019-04-09T13:20:00.000000000', '2019-04-09T13:25:00.000000000',\n",
       "       '2019-04-09T13:30:00.000000000', '2019-04-09T13:35:00.000000000',\n",
       "       '2019-04-09T13:40:00.000000000', '2019-04-09T13:45:00.000000000',\n",
       "       '2019-04-09T13:50:00.000000000', '2019-04-09T13:55:00.000000000'],\n",
       "      dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.12769, 1.12789, 1.12776, 1.12789, 1.1279 , 1.12777, 1.128  ,\n",
       "       1.12823, 1.12796, 1.12786, 1.1276 , 1.1279 , 1.12837, 1.12836,\n",
       "       1.12805, 1.12809, 1.12829, 1.12829, 1.12826, 1.12813, 1.12842,\n",
       "       1.12821, 1.12812, 1.12797])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData['High'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib qt\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeplot():\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])\n",
    "    #ax.autoscale(enable=False,axis='y')\n",
    "    ax.scatter(myData.index.values,myData['High'].values)\n",
    "    \n",
    "def makeplot_noas():\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])\n",
    "    #ax.autoscale(enable=False,axis='y')\n",
    "    miny = 1.1274 \n",
    "    maxy = 1.1285\n",
    "    ax.set_ylim(ymin=miny,ymax=maxy)\n",
    "    minx = min(myData.index.values)\n",
    "    maxx = max(myData.index.values)\n",
    "    ax.set_xlim(xmin=minx,xmax=maxx)\n",
    "    ax.scatter(myData.index.values,myData['High'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeplot_noas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
